What this book covers

Chapter 1, From Business Strategy to Data Strategy to Data Stewardship, explores the transformation from business strategy to data strategy and, ultimately, to data stewardship, emphasizing skill development and execution practices for effective data management programs.

Chapter 2, How Data Stewardship Can Help Your Organization, explains data stewardship’s role in enhancing competitive advantage by improving data accuracy, security, and management, leading to cost reduction and increased customer satisfaction.

Chapter 3, Getting Started with the Data Stewardship Program, guides you on starting a data stewardship program, highlighting stakeholder buy-in, key program elements, and strategies for addressing initial data management challenges.

Chapter 4, Developing a Comprehensive Data Management Strategy, takes you through creating a data management strategy, from assessing current states to defining future goals and executing governance, quality, and security plans.

Chapter 5, People, Process, Technology, delves into the interplay of people, processes, and technology in data stewardship, showing ROI and improving practices through automation and artificial intelligence (AI).

Chapter 6, Establishing a Data Governance Structure, outlines the steps for establishing a data governance structure, fostering a data culture, and defining KPIs, and provides tools for creating and measuring a successful governance program.

Chapter 7, Data Steward Roles and Responsibilities, defines data steward roles, emphasizing data quality, access control, security, and compliance, equipping you with frameworks for effective data governance.

Chapter 8, Effective Data Stewardship, discusses principles of effective data stewardship, including data ownership, role assignment, accountability, and leveraging continuous training for a successful data governance roadmap.

Chapter 9, Supercharge Data Governance and Stewardship with GPT, explores using GPT for enhancing data governance and stewardship, automating management tasks, and ensuring data privacy, security, and the prevention of bias in AI applications.

Chapter 10, Data Stewardship Best Practices, shares best practices for data stewardship, aligning mindsets, upskilling teams, fostering accountability, and ownership, and integrating emerging technologies for effective data management programs.

Chapter 11, Theory versus Real Life, addresses the gap between theory and real-life data stewardship, emphasizing collaboration, continuous monitoring, and improvement for bridging this divide and ensuring program success.

Chapter 12, Case Studies, presents two case studies on effective data stewardship: the Bank of East Asia fosters a data-driven culture for operational efficiency, while Fencore’s solution streamlines fund management, showcasing the practical impact of data strategies.